The emotional labour of quality improvement work in end of life care: a qualitative study of Patient and Family Centred Care (PFCC) in England

A Qualitative Study Exploring Patient Shadowing as a Method to Improve Patient-Centred Care: Ten Principles for a New Gold Standard

BACKGROUND

In recent years there has been an increased emphasis on patient experience as a dimension of quality in healthcare, and subsequently a drive to understand care from the patient's perspective. Patient shadowing is an approach which has been used in service improvement projects, but its potential as a Quality Improvement (QI) method has not been studied in practical and replicable detail. This research aimed to do this, and to produce clear guidance for future teams.

METHODS

Qualitative interviews were conducted with 20 clinical and non-clinical participants of a national quality improvement programme in England, which focused on improving the experience of patients at the end of life. All participants had shadowed patients. Data were analysed using thematic analysis.

RESULTS

There were two broad themes: 1) The process of shadowing: how participants went about shadowing, adopting different approaches and making judgements about the care they observed, and any challenges they had encountered. 2) The impact of shadowing: on the engagement and motivation of those who shadowed, and in terms of service changes to benefit patients and their families.

CONCLUSION

The findings led to a new set of 'gold standard' principles to benefit both staff and patients where shadowing is used as QI method. These, together with new guidance, will ensure that shadowing is conducted as a team exercise, and that all those involved are more robustly prepared and supported, and that its purpose as a method to improve patient experience will be better understood.

‘Using humanity to change systems’ – understanding the work of online feedback moderation: A case study of Care Opinion Scotland

Objective

To gain a deeper understanding of online patient feedback moderation through the organisation of Care Opinion in Scotland.

Methods

An ethnographic study, initially using in-person participant observations, switching to remote methods due to the pandemic. This involved the use of remote observations and interviews. Interviews were carried out with the whole Scottish team (n = 8).

Results

Our results identify three major themes of work found in online patient feedback moderation. The first is process work, where moderators make decisions on how to edit and publish stories. The second is emotional labour from working with healthcare experiences and with NHS staff. The third is the brokering/mediation role of Care Opinion, where they must manage the relationships between authors, subscribing healthcare providers and Scottish Government. Our results also capture that these different themes are not independent and can at times influence the others.

Conclusion

Our results build on previous literature on Care Opinion and provide novel insights into the emotional and brokering/mediation work they undertake. Care Opinion holds a unique position, where they must balance the interests of the key stakeholders. Care Opinion holds the power to amplify authors’ voices but the power to make changes to services lies with NHS staff and services. Online moderation work is complex, and moderators require support to carry out their work especially given the emotional impact. Further research is planned to understand how patient stories are used by NHS Scotland, and the emotional labour involved with stories, from both the author and NHS staff perspective.

Natural language processing to measure the frequency and mode of communication between healthcare professionals and family members of critically ill patients

OBJECTIVE

To apply natural language processing (NLP) techniques to identify individual events and modes of communication between healthcare professionals and families of critically ill patients from electronic medical records (EMR).

MATERIALS AND METHODS

Retrospective cohort study of 280 randomly selected adult patients admitted to 1 of 15 intensive care units (ICU) in Alberta, Canada from June 19, 2012 to June 11, 2018. Individual events and modes of communication were independently abstracted using NLP and manual chart review (reference standard). Preprocessing techniques and 2 NLP approaches (rule-based and machine learning) were evaluated using sensitivity, specificity, and area under the receiver operating characteristic curves (AUROC).

RESULTS

Over 2700 combinations of NLP methods and hyperparameters were evaluated for each mode of communication using a holdout subset. The rule-based approach had the highest AUROC in 65 datasets compared to the machine learning approach in 21 datasets. Both approaches had similar performance in 17 datasets. The rule-based AUROC for the grouped categories of patient documented to have family or friends (0.972, 95% CI 0.934-1.000), visit by family/friend (0.882 95% CI 0.820-0.943) and phone call with family/friend (0.975, 95% CI: 0.952-0.998) were high.

DISCUSSION

We report an automated method to quantify communication between healthcare professionals and family members of adult patients from free-text EMRs. A rule-based NLP approach had better overall operating characteristics than a machine learning approach.

CONCLUSION

NLP can automatically and accurately measure frequency and mode of documented family visitation and communication from unstructured free-text EMRs, to support patient- and family-centered care initiatives.